Testing macro trading factors

February 11, 2023

The recorded history of modern financial markets and macroeconomic developments is limited. Hence, statistical analysis of macro trading factors often relies on panels, sets of time series across different currency areas. However, country experiences are not independent and subject to common factors. Simply stacking data can lead to “pseudo-replication” and overestimated significance of correlation. A better method is to check significance through panel regression models with period-specific random effects. This technique adjusts targets and features of the predictive regression for common (global) influences. The stronger these global effects, the greater the weight of deviations from the period-mean in the regression. In the presence of dominant global effects, the test for the significance of a macro factor would rely mainly upon its ability to explain cross-sectional target differences. Conveniently, the method automatically accounts for the similarity of experiences across markets when assessing the significance and, hence, can be applied to a wide variety of target returns and features. Examples show that the random effects method can deliver a quite different and more plausible assessment of macro factor significance than simplistic statistics based on pooled data.

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Identifying the drivers of the commodity market

November 26, 2022

Commodity futures returns are correlated across many different raw materials and products. Research has identified various types of factors behind this commonality: [i] macroeconomic changes, [ii]  financial market trends, and [iii] shifts in general uncertainty. A new paper proposes to estimate the strength and time horizon of these influences through mixed-frequency vector autoregression. Mixed-frequency Granger causality tests can assess the interaction of monthly, weekly, and daily data without aggregating to the lowest common frequency and losing information. An empirical analysis for 37 commodity futures from all major sectors, based on mixed-frequency Granger causality tests,  suggests that macroeconomic changes are the dominant common driver of monthly commodity returns, while financial market variables exercise commanding influence at a daily frequency.

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Identifying market regimes via asset class correlations

October 28, 2022

A recent paper suggests identifying financial market regimes through the correlations of asset class returns. The basic idea is to calculate correlation matrixes for sliding time windows and then estimate pairwise similarities. This gives a matrix of similarity across time. One can then perform principal component analysis on this similarity matrix and extract the “axes” of greatest relevance. Subsequently, one can cluster the dates in the new reduced space, for example by a K-means method, and choose an optimal number of clusters. These clusters would be market regimes. Empirical analyses of financial markets over the last 20-100 years identify 6-7 market regimes.

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Copulas and trading strategies

July 21, 2022

Reliance on linear correlation coefficients and joint normal distribution of returns in multi-asset trading strategies can be badly misleading. Such conventions often overestimate diversification benefits and underestimate drawdowns in times of market stress. Copulas can describe the joint distribution of multiple returns or price series more realistically. They separate the modelling of dependence structures from the marginal distributions of the individual returns. Copulas are particularly suitable for assessing joint tail distributions, such as the behaviour of portfolios in extreme market states. This is when risk management matters most. A critical choice is the appropriate marginal distributions and copula functions based on the stylized features of contract return data. Multivariate distributions based on these assumptions can be simulated in Python.

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Predicting volatility with neural networks

March 19, 2022

Predicting realized volatility is critical for trading signals and position calibration. Econometric models, such as GARCH and HAR, forecast future volatility based on past returns in a fairly intuitive and transparent way. However, recurrent neural networks have become a serious competitor. Neural networks are adaptive machine learning methods that use interconnected layers of neurons. Activations in one layer determine the activations in the next layer. Neural networks learn by finding activation function weights and biases through training data. Recurrent neural networks are a class of neural networks designed for modeling sequences of data, such as time series. And specialized recurrent neural networks have been developed to retain longer memory, particularly LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Unit). The advantage of neural networks is their flexibility to include complex interactions of features, non-linear effects, and various types of non-price information.

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Statistical learning and macro trading: the basics

February 5, 2022

The rise of data science and statistical programming has made statistical learning a key force in macro trading. Beyond standard price-based trading algorithms, statistical learning also supports the construction of quantamental systems, which make the vast array of fundamental and economic time series “tradable” through cleaning, reformatting, and logical adjustments. Fundamental economic developments are poised to play a growing role in the statistical trading and support models of market participants. Machine learning methods automate the process and are a basis for reliable backtesting and efficient implementation.

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Measuring the value-added of algorithmic trading strategies

October 16, 2021

Standard performance statistics are insufficient and potentially misleading for evaluating algorithmic trading strategies. Metrics based on prediction errors mistakenly assume that all errors matter equally. Metrics based on classification accuracy disregard the magnitudes of errors. And traditional performance ratios, such as Sharpe, Sortino and Calmar are affected by factors outside the algorithm, such as asset class performance, and rely on the normal distribution of returns. Therefore, a new paper proposes a discriminant ratio (‘D-ratio’) that measures an algorithm’s success in improving risk-adjusted returns versus a related buy-and-hold portfolio. Roughly speaking, the metric divides annual return by a value-at-risk metric that does not rely on normality and then divides it by a similar ratio for the buy-and-hold portfolio. The metric can be decomposed into the contributions of return enhancement and risk reduction.

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Ten things investors should know about nowcasting

September 4, 2021

Nowcasting in financial markets is mainly about forecasting forthcoming data reports, particularly GDP releases. However, nowcasting models are more versatile and can be used for a range of market-relevant information, including inflation, sentiment, weather, and harvest conditions. Nowcasting is about information efficiency and is particularly suitable for dealing with big messy data. The underlying models typically condense large datasets into a few underlying factors. They also tackle mixed frequencies of time series and missing data. The most popular model class for nowcasting is factor models: there are different categories of these that produce different results. One size does not fit all purposes. Also, factor models have competitors in the nowcasting space, such as Bayesian vector autoregression, MIDAS models and bridge regressions. The reason why investors should understand their nowcasting models is that they can do a lot more than just nowcasting: most models allow tracking latent trends, spotting significant changes in market conditions, and quantifying the relevance of different data releases.

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Statistical arbitrage risk premium

April 17, 2021

Any asset can use a portfolio of similar assets to hedge against its factor exposure. The factor residual risk of the hedged position is called statistical arbitrage risk. Consequently, the statistical arbitrage risk premium is the expected return of such a hedged position. A recent paper shows that both theoretically and empirically this premium rises in the stock’s statistical arbitrage risk. ‘Unique’ stocks have higher excess returns than ‘ubiquitous’ stocks. The estimated premium is therefore a valid basis for investment strategies. Statistical arbitrage risk can be estimated by using ‘elastic net’ estimation and related machine learning. This method selects a relatively small hedge portfolio from a large array of candidate stocks.

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What traders should know about seasonal adjustment

December 26, 2020

The purpose of seasonal adjustment is to remove seasonal and calendar effects from economic time series. It is a common procedure but also a complex one, with side effects. Seasonal adjustment has two essential stages. The first accounts for deterministic effects by means of regression and selects a general time series model. The second stage decomposes the original time series into trend-cycle, seasonal, calendar and irregular components.
Seasonal adjustment does not generally improve the quality of economic data. There is always some loss of information. Also, it is often unclear which calendar effects have been removed. And sometimes seasonal adjustment is just adding noise or fails to remove all seasonality. Moreover, seasonally adjusted data are not necessarily good trend indicators. By design, they do not remove noise and outliers. And extreme weather events or public holiday patterns are notorious sources of distortions. Estimated trends at the end of the series are subject to great uncertainty. Furthermore, seasonally adjusted time series are often revised and can be source of bias if these data are used for trading strategy backtests.

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asset management Asset Purchase Program banks bond returns bond yields bubbles capital regulation carry China collateral commodities credit returns default ECB economic data EM equity returns euro area crisis Fed financial crisis FX FX interventions FX returns government debt government deficits herding inflation information insurances Japan leverage liquidity machine learning market turmoil price factors regulation risk management shadow banking statistical methods term structure trend following variance risk premium VIX volatility ZLB
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Macrosynergy is a London based macroeconomic research and technology company whose founders have developed and employed macro quantamental investment strategies in liquid, tradable asset classes, across many markets and for a variety of different factors to generate competitive, uncorrelated investment returns for institutional investors for two decades. Our quantitative-fundamental (quantamental) computing system tracks a broad range of real-time macroeconomic trends in developed and emerging countries, transforming them into macro systematic quantamental investment strategies. In June 2020 Macrosynergy and J.P. Morgan started a collaboration to scale the quantamental system and to popularize tradable economics across financial markets.

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